This module addresses topics of:

Cyber Security:
Security Models and Computer Security Properties
Security Policies and Standards
Access Control
Information Risk Management
Cryptography Fundamentals
Network Security
Risk Assessment
Cyber research skills
Linux Introduction
Introduction to Ethical Hacking Concepts
Introduction to Operating System Security
Introduction to Malware
Introduction to Cloud Computing Security
DDoS and DoS attacks
Disaster Recovery
Sustainability and costing based models
Legal, social, ethical and professional issues
Accessibility and inclusivity in relation to the cyber domain

Artificial Intelligence:
Philosophical foundations: Artificial Intelligence (AI) vs. Machine Learning (ML)
Learning paradigms: Supervised learning, unsupervised learning and reinforcement learning
Real-life applications of ML – case studies
Inclusivity and accessibility issues within the AI domain
The principles of an Artificial Neural Network (ANN)
Statistical principles: Correlation and regression
Classification techniques: Bayes Theorem, Naïve Bayes and K-means clustering
Principles of evolutionary/genetic algorithms
Legal, social, ethical and professional issues
AI research skills

BCS / TechSkills / Employability elements:
Evaluation of systems: Students will undertake a research themed focus review of both the Cyber Security and Artificial Intelligence areas
Legal, social, ethical and professional issues: Are examined in depth in both reports of the assessment

REPORT - You are required to conduct academic research in relation to a relevant cyber security topic. You will produce a research report of 2000 words in which you demonstrate your ability to conduct academic research as well as your understanding of core cyber security concepts, technologies and techniques. As part of the report, you must clearly show how cyber security fits as part of the wider IT landscape across commercial and domestic environments.

REPORT – You are required to choose an aspect of machine learning that you have studied on this module, for example (but not limited to) artificial neural networks, Bayesian classification, computer vision or the ethical dimensions of AI and write a related essay. The essay will specifically need to identify the Machine Learning aspects and relate these to the wider field of Artificial Intelligence. The report requires you to write up and discuss related key algorithms. In terms of written style it is expected that you read around the topic, compare methods, and provide clear reflection as to information included within the report.

The module delivery will consist of face-to-face lectures used to present essential theory coupled with practicals in order you explore and learn equally from a hands-on approach. To aid in your assessment work there will be classroom led activity such as seminars in order you explore topics in depth with others.

1. Identify and describe basic cyber security concepts and techniques.

Knowledge & Understanding
Application & Problem-Solving

2. Explain how cyber security fits into the wider IT landscape in both commercial and domestic scenarios.

Communication
Personal Development & Entrepreneurship

3. Describe the differences between artificial intelligence and machine learning and be able to define and give examples of different machine learning techniques.

Communication
Reflection

4. Understand the operation of key algorithms used for machine learning, data classification, and applying Artificial Neural Networks (ANN).

Knowledge and Understanding
Reflection

Access to the Cyber Lab

Standard PC access for Artificial Intelligence course components

Parikha, D. (2025) Machine Learning Essentials You Always Wanted to Know: A Hands-On Beginner's Guide to Mastering AI, Supervised, Unsupervised, and Deep Learning Algorithms (Self-Learning Management Series), Vibrant Publishers

Brasil, J. (2025) Now Machine Learning Vol1: Supervised Learning: Mastering Regression, Classification, and Predictive Modelling with Python, Now Machine Learning

Casey, E. (2024), Digital Evidence and Computer Crime: Forensic Science, Computers and the Internet, 4th Edition, Academic Press.

Johansen, G. (2024), Digital Forensics and Incident Response: Incident Response Tools and Techniques for Effective Cyber Threat Response, 4th Edition, Packt Publishing.

Kizza, J.M. (2024), Guide to Computer Network Security, 6th Edition, Springer

In this module, you will learn some fundamental mathematical principles of artificial intelligence and machine learning and learn and practice several techniques for building and testing machine learning models. You will also learn about the principles of an Artificial Neural Network and be able to articulate the operation of the fundamental components and how they interact. You will learn about evolutionary algorithms, techniques for building adaptive and self-learning ML models. You will also learn about how machine learning can be applied to search for optimal solutions using multiple agents. You will also learn fundamental principles of cyber security, as well as how these fit into the wider computing landscape. You will also learn about core cyber security technologies and techniques, and how these are used to secure a wide range of computational systems and networks.